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Practical Predictive Analytics

You're reading from   Practical Predictive Analytics Analyse current and historical data to predict future trends using R, Spark, and more

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Product type Paperback
Published in Jun 2017
Publisher Packt
ISBN-13 9781785886188
Length 576 pages
Edition 1st Edition
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Author (1):
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Ralph Winters Ralph Winters
Author Profile Icon Ralph Winters
Ralph Winters
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Table of Contents (13) Chapters Close

Preface 1. Getting Started with Predictive Analytics FREE CHAPTER 2. The Modeling Process 3. Inputting and Exploring Data 4. Introduction to Regression Algorithms 5. Introduction to Decision Trees, Clustering, and SVM 6. Using Survival Analysis to Predict and Analyze Customer Churn 7. Using Market Basket Analysis as a Recommender Engine 8. Exploring Health Care Enrollment Data as a Time Series 9. Introduction to Spark Using R 10. Exploring Large Datasets Using Spark 11. Spark Machine Learning - Regression and Cluster Models 12. Spark Models – Rule-Based Learning

Scrubbing and cleaning the data


Here comes the cleaning part!

Print some of the groceries contained within the description field of OnlineRetail:

kable(OnlineRetail$Description[1:5],col.names=c("Grocery Item Descriptions")) 
|Grocery Item Descriptions                 |  
|:-----------------------------------------| 
|WHITE HANGING HEART T-LIGHT HOLDER        | 
|METAL METAL LANTERN                       | 
|CREAM CUPID HEARTS COAT HANGER            | 
|KNITTED UNION FLAG HOT WATER BOTTLE       | 
|RED WOOLLY HOTTIE WHITE HEART.            | 

Although each line contains a separate grocery item, the items are in a uniform format, that is, the number of words describing each item can vary, and some words are adjectives and some are nouns. Additionally, the retailer may deem certain words to be irrelevant to a particular marketing campaign (such as colors, or sizes, which may be standard across all products). This type of data can be referred to as semi-structured data, since it incorporates certain...

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